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Probabilistic frontier regression models for binary type output data

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  • Meena Badade
  • T. V. Ramanathan

Abstract

This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as ‘selected’ category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.

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  • Meena Badade & T. V. Ramanathan, 2019. "Probabilistic frontier regression models for binary type output data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(13), pages 2460-2480, October.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2460-2480
    DOI: 10.1080/02664763.2019.1597838
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    Cited by:

    1. Meena Badade & T. V. Ramanathan, 2022. "Probabilistic Frontier Regression Models for Count Type Output Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 235-260, September.
    2. Meena Badade & T. V. Ramanathan, 2020. "Probabilistic frontier regression model for multinomial ordinal type output data," Journal of Productivity Analysis, Springer, vol. 53(3), pages 339-354, June.

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